Second-order biases of maximum likelihood estimates in overdispersed generalized linear models
AbstractIn this paper, we derive general formulae for second-order biases of maximum likelihood estimates in overdispersed generalized linear models, thus generalizing results by Cordeiro and McCullagh (J. Roy. Statist. Soc. Ser. B 53 (1991) 629), and Botter and Cordeiro (Statist. Comput. Simul. 62 (1998) 91). Our formulae cover many important and commonly used models and are easily implemented by means of supplementary weighted linear regressions. They are also simple enough to be used algebraically to obtain several closed-form expressions in special models. The practical use of such formulae is illustrated in a simulation study.
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Bibliographic InfoArticle provided by Elsevier in its journal Statistics & Probability Letters.
Volume (Year): 55 (2001)
Issue (Month): 3 (December)
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Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description
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- Patriota, Alexandre G. & Lemonte, Artur J., 2009. "Bias correction in a multivariate normal regression model with general parameterization," Statistics & Probability Letters, Elsevier, vol. 79(15), pages 1655-1662, August.
- Barreto-Souza, Wagner & Vasconcellos, Klaus L.P., 2011. "Bias and skewness in a general extreme-value regression model," Computational Statistics & Data Analysis, Elsevier, vol. 55(3), pages 1379-1393, March.
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